Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm
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Quantum Physics arXiv:2602.20407 (quant-ph) [Submitted on 23 Feb 2026] Title:Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm Authors:Lucas A. M. Rattighieri, Pedro M. Prado, Marcos C. de Oliveira, Felipe F. Fanchini View a PDF of the paper titled Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm, by Lucas A. M. Rattighieri and 3 other authors View PDF HTML (experimental) Abstract:Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to sufficiently concentrate probability on low-energy configurations. We introduce Measurement-Guided Initialization (MGI), an iterative strategy that uses measurement outcomes from previous executions to update the initialization of subsequent runs. The method extracts single-qubit marginal probabilities from dominant measurement outcomes and prepares a biased product-state initialization, allowing information obtained during optimization to be reused without introducing classical parameter optimization. We implement this approach in the context of the Feedback-Based Algorithm for Quantum Optimization (FALQON) and evaluate its performance on weighted MaxCut instances. Numerical results show that measurement-guided initialization improves the performance of shallow-depth circuits and enables iterative refinement toward high-quality solutions while preserving the non-variational structure of the algorithm. These results indicate that measurement statistics can be exploited to improve shallow quantum optimization protocols compatible with NISQ devices. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.20407 [quant-ph] (or arXiv:2602.20407v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.20407 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Lucas Rattighieri [view email] [v1] Mon, 23 Feb 2026 23:07:11 UTC (507 KB) Full-text links: Access Paper: View a PDF of the paper titled Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm, by Lucas A. M. Rattighieri and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-02 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)